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A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology

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Abstract

In recent times, solar energy has become a highly promising source of energy and one of the most regular types of sustainable energy. Forecasting the availability of solar energy has become a concern of many studies because of the intermittent characteristics of solar power. This study proposes a new stacked generalization methodology for predicting long-term photovoltaic power. In the proposed methodology, the base learners used consisted of group method of data handling (GMDH), least squares support vector machine (LSSVM), emotional neural network (ENN), and radial basis function neural network (RBFNN). The backpropagation neural network (BPNN) served as the meta-learner in the stacked approach. The proposed stacked generalization method showed superiority over the four standalone state-of-the-art methods (GMDH, LSSVM, ENN, and RBFNN) when tested with real data using performance indicators such as Bayesian information criteria (BIC), percent mean average relative error (PMARE), Legates and McCabe index (LM), mean absolute error, and root mean square error. The stacked model had the lowest BIC and PMARE values of 10,417.54 and 0.3617% for testing results. It also had the highest LM score of 0.996711 as compared with the benchmark standalone models, confirming its strength in forecasting photovoltaic power.

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References

  • Amarasinghe, P. A. G. M., Abeygunawardana, N. S., Jayasekara, T. N., Edirisinghe, E. A. J. P., & Abeygunawardane, S. K. (2020). Ensemble models for solar power forecasting—a weather classification approach. AIMS Energy, 8(2), 252–271. https://doi.org/10.3934/energy.2020.2.252

    Article  Google Scholar 

  • Baghbani, F, Akbarzadeh-T, M. R., Naghibi-Sistani, M. B., & Akbarzadeh, A. (2020). Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems. Engineering Application of Artificial Intelligence, 89, 103447.

  • Bigdeli, N., Borujeni, M. S., & Afshar, K. (2017). Time series analysis and short-term forecasting of solar irradiation, a new hybrid approach. Swarm Evolutionary Computation, 34, 75–88.

    Article  Google Scholar 

  • Chandra, S., Gaur, P., & Pathak, D. (2020). Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system. Computer & Electrical Engineering, 86, 106730.

  • Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81, 912–928.

    Article  Google Scholar 

  • de Freitas Viscondi, G., & Alves-Souza, S. N. (2019). A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting. Sustainable Energy Technologies and Assessments, 31, 54–63.

    Article  Google Scholar 

  • Deng, F., Deng, Z., Liang, H., Wang, L., Hu, H., & Xu, Y. (2021a). Life prediction of slotted screen based on back-propagation neural network. Engineering Failure Analysis 119, 104909.

  • Deng, Y., Zhou, X., Shen, J., Xiao, G., Hong, H., Lin, H., Wu, F., & Liao, B. Q. (2021b). New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. Science of the Total Environment, 772, 145534.

  • Dong, Z., Yang, D., Reindl, T., & Walsh, W. M. (2015). A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy, 82, 570–577.

    Article  Google Scholar 

  • Duddu, V. R., Pulugurtha, S. S., Mane, A. S., & Godfrey, C. (2020). Back-propagation neural network model to predict visibility at a road link-level. Transportation Research Interdisciplinary Perspectives 8, 100250.

  • Ebtehaj, I., Bonakdari, H., & Gharabaghi, B. (2018). Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement, 116, 473–482.

    Article  Google Scholar 

  • Eseye, A. T., Zhang, J., & Zheng, D. (2018). Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renewable Energy, 118, 357–367.

    Article  Google Scholar 

  • Feng, Y. Q., Liu, Y. Z., Wang, X., He, Z. X., Hung, T. C., Wang, Q., & Xi, H., (2020). Performance prediction and optimization of an organic Rankine cycle (ORC) for waste heat recovery using back propagation neural network. Energy Conversion and Management, 226, 113552.

  • Garud, K. S., Jayaraj, S., & Lee, M. Y. (2021). A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. International Journal of Energy Research, 45(1), 6–35.

    Article  Google Scholar 

  • Ghadimloozadeh, S., Sohrabi, M. R., & Fard, H. K. (2021). Development of rapid and simple spectrophotometric method for the simultaneous determination of anti-parkinson drugs in combined dosage form using continuous wavelet transform and radial basis function neural network. Optik, 242, 167088.

  • Ghimire, S., Deo, R. C., Raj, N., & Mi, J. (2019). Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Applied Energy, 253, 113541.

  • Gu, B., Shen, H., Lei, X., Hu, H., & Liu, X. (2021). Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method. Applied Energy, 299, 117291.

  • Guermoui, M., Bouchouicha, K., Bailek, N., & Boland, J. W. (2021). Forecasting intra-hour variance of photovoltaic power using a new integrated model. Energy Conversion and Management, 245, 114569.

  • Heydari, A., Astiaso Garcia, D., Keynia, F., Bisegna, F., & De Santoli, L. (2019a). Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting. Energy Sources, Part b, Economy, Planning, and Policy, 14(10–12), 341–358.

    Article  Google Scholar 

  • Heydari, A., Keynia, F., Garcia, D. A., & De Santoli, L. (2019b). Mid-term load power forecasting considering environment emission using a hybrid intelligent approach. In 2018 5th International Symposium on Environmental-Friendly Energies and Applications (EFEA), (1–5). IEEE . https://doi.org/10.1109/EFEA.2018.8617079

  • Huld, T., Müller, R., & Gambardella, A. (2012). A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy, 86(6), 1803–1815.

    Article  Google Scholar 

  • Jaseena, K. U., & Kovoor, B. C. (2020). A hybrid wind speed forecasting model using stacked autoencoder and LSTM. Journal of Renewable and Sustainable Energy, 12(2), 023302.

  • Jia, R., Lv, Y., Wang, G., Carranza, E. J. M., Chen, Y., Wei, C., & Zhang, Z. (2021). A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China). Computer & Geoscience 151, 104754.

  • Jiang, Y., Liu, S., Zhao, N., Xin, J., & Wu, B. (2020). Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model. Energy Conversion and Management, 220, 113076.

  • Ke, Y., Yao, C., Song, E., Dong, Q., & Yang, L. (2021). Intelligent fault diagnosis method of common rail injector based on composite hierarchical dispersion entropy and improved least squares support vector machine. Digital Signal Processing, 114, 103054.

  • Keyvan, K., Sohrabi, M. R., & Motiee, F. (2021). An intelligent method based on feed-forward artificial neural network and least square support vector machine for the simultaneous spectrophotometric estimation of anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 263, 120190.

  • Khan, A. T., Khan, A. R., Li, S., Bakhsh, S., Mehmood, A., & Zaib, J. (2021). Optimally configured Gated Recurrent Unit using Hyperband for the long-term forecasting of photovoltaic plant. Renewable Energy Focus, 39, 49–58.

    Article  Google Scholar 

  • Korkmaz, D. (2021). SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting. Applied Energy, 300, 117410.

  • Kushwaha, V., & Pindoriya, N. M. (2019). A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. Renewable Energy, 140, 124–139.

    Article  Google Scholar 

  • Li, L. L., Wen, S. Y., Tseng, M. L., & Wang, C. S. (2019). Renewable energy prediction: A novel short-term prediction model of photovoltaic output power. Journal of Cleaner Production, 228, 359–375.

    Article  Google Scholar 

  • Li, P., Zhou, K., Lu, X., & Yang, S. (2020). A hybrid deep learning model for short-term PV power forecasting. Applied Energy, 259, 114216.

  • Liu, C. (2022). Risk prediction of digital transformation of manufacturing supply chain based on Principal Component Analysis and Backpropagation Artificial Neural. Network, 61(1), 775–784.

    Google Scholar 

  • Liu, F., Lu, Y., & Cai, M. (2020). A hybrid method with adaptive sub-series clustering and attention-based stacked residual LSTMs for multivariate time series forecasting. IEEE Access, 8, 62423–62438.

    Article  Google Scholar 

  • Liu, H., Duan, Z., Wu, H., Li, Y., & Dong, S. (2019). Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network. Measurement 148, 106971.

  • Lu, H. J., & Chang, G. W. (2018). A hybrid approach for day-ahead forecast of PV Power generation. IFAC-PapersOnLine, 51(28), 634–638.

    Article  Google Scholar 

  • Majumder, I., Dash, P. K., & Bisoi, R. (2018). Variational mode decomposition based low rank robust kernel extreme learning machine for solar irradiation forecasting. Energy Conversion and Management, 171, 787–806.

    Article  Google Scholar 

  • Mohammadi, M. R., Hemmati-Sarapardeh, A., Schaffie, M., Husein, M. M., & Ranjbar, M. (2021). Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery. Journal of Petroleum Science and Engineering, 205, 108836.

  • Monjoly, S., André, M., Calif, R., & Soubdhan, T. (2017). Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy, 119, 288–298.

    Article  Google Scholar 

  • Naimi, A. I., & Balzer, L. B. (2018). Stacked generalization: An introduction to super learning. European Journal of Epidemiology, 33(5), 459–464. https://doi.org/10.1007/s10654-018-0390-z.

  • Nguyen, H., Bui, X. N., Tran, Q. H., Nguyen, H. A., Nguyen, D. A., Hoa, L. T. T., & Le, Q. T. (2021). Prediction of ground vibration intensity in mine blasting using the novel hybrid MARS–PSO–MLP model. Engineering with Computers, 1–19. https://doi.org/10.1007/s00366-021-01332-8.

  • Niu, D., Wang, K., Sun, L., Wu, J., & Xu, X. (2020). Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Applied Soft Computing, 93, 106389.

  • Nourani, V., Gökçekuş, H., Umar, I. K., & Najafi, H. (2020). An emotional artificial neural network for prediction of vehicular traffic noise. Science of the Total Environment, 707, 136134.

  • Ofori-Ntow, E. J., Ziggah, Y. Y., & Relvas, S. (2021). Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting. Sustainable Cities and Society, 66, 102679.

  • Prasad, R., Ali, M., Xiang, Y., & Khan, H. (2020). A double decomposition-based modelling approach to forecast weekly solar radiation. Renewable Energy, 152, 9–22.

    Article  Google Scholar 

  • Qu, J., Qian, Z., & Pei, Y. (2021). Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern. Energy, 232, 120996.

  • Rostami, A., Hemmati-Sarapardeh, A., Karkevandi-Talkhooncheh, A., Husein, M. M., Shamshirband, S., & Rabczuk, T. (2019). Modeling heat capacity of ionic liquids using group method of data handling: A hybrid and structure-based approach. International Journal of Heat and Mass Transfer, 129, 7–17.

    Article  Google Scholar 

  • Rozario, A. P. R., & Devarajan, N. (2021). Monitoring the quality of water in shrimp ponds and forecasting of dissolved oxygen using Fuzzy C means clustering based radial basis function neural networks. Journal of Ambient Intelligence and Humanized Computing, 12(5), 4855–4862.

    Article  Google Scholar 

  • Salcedo-Sanz, S., Deo, R. C., Cornejo-Bueno, L., Camacho-Gómez, C., & Ghimire, S. (2018). An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Applied Energy, 209, 79–94.

    Article  Google Scholar 

  • Shahid, A. H., & Singh, M. P. (2020). A novel approach for coronary artery disease diagnosis using hybrid particle Swarm Optimization based Emotional Neural Network. Biocybernetics and Biomedical Engineering, 40(1), 1568–1585.

    Article  Google Scholar 

  • Shokouhi, S., & Sohrabi, M. R. (2021). Net analyte signal and radial basis function neural network for development spectrophotometry method for the simultaneous determination of metformin and sitagliptin in anti-diabetic commercial tablet. Optik, 243, 167518.

  • Singh, N. K., Tripathy, M., & Singh, A. K. (2011). Application of RBFNN for Short Term Load and Application of RBFNN for Short Term Load and Price Forecasting.

  • Singh, N. K., Singh, A. K., & Tripathy, M. (2014). A comparative study of BPNN, RBFNN and ELMAN neural network for short-term electric load forecasting: A case study of Delhi region. In: 2014 9th International Conference on Industrial and Information Systems (ICIIS). IEEE, pp 1–6. https://doi.org/10.1109/ICIINFS.2014.7036502

  • Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459–497.

    Article  Google Scholar 

  • Sun, S., Wang, S., Zhang, G., & Zheng, J. (2018). A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Solar Energy, 163, 189–199.

    Article  Google Scholar 

  • Sun, H. J., Wu, Y. X., Wu, Z. F., Han, F., Yang, M., & Wang, Y. Q. (2021). Optimization of vacuum assisted heat reflux extraction process of radix isatidis using least squares-support vector machine algorithm. Phytochemistry Letters, 43, 108–113.

    Article  Google Scholar 

  • Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.

    Article  Google Scholar 

  • Takeda, H., Tamura, Y., & Sato, S. (2016). Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy, 104, 184–198.

    Article  Google Scholar 

  • Tan, Z., De, G., Li, M., Lin, H., Yang, S., Huang, L., & Tan, Q. (2020). Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine. Journal of Cleaner Production, 248, 119252.

  • Temeng, V. A., Ziggah, Y. Y., & Arthur, C. K. (2020). A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network. International Journal of Mining Science and Technology, 30(5), 683–689.

    Article  Google Scholar 

  • Tian, Z. (2020). Backtracking search optimization algorithm-based least square support vector machine and its applications. Engineering Applications of Artificial Intelligence, 94, 103801.

  • Ting, K. M., & Witten, I. H. (1997). Stacked Generalization: When does it work? 866–871

  • VanDeventer, W., Jamei, E., Thirunavukkarasu, G. S., Seyedmahmoudian, M., Soon, T. K., Horan, B., Mekhilef, S., & Stojcevski, A. (2019). Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 140, 367–379.

    Article  Google Scholar 

  • Walton, R., Binns, A., Bonakdari, H., Ebtehaj, I., & Gharabaghi, B. (2019). Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling. Journal of Hydrology, 575, 671–689.

    Article  Google Scholar 

  • Wang, K., Qi, X., & Liu, H. (2019). Photovoltaic power forecasting based LSTM-Convolutional Network. Energy 189, 116225.

  • Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1

  • Yang, Z., Mourshed, M., Liu, K., Xu, X., & Feng, S. (2020). A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting. Neurocomputing, 397, 415–421.

    Article  Google Scholar 

  • Youcefi, M. R., Hadjadj, A., & Boukredera, F. S. (2021). New model for standpipe pressure prediction while drilling using Group Method of Data Handling. Petroleum. https://doi.org/10.1016/j.petlm.2021.04.003

    Article  Google Scholar 

  • Zang, H., Liu, L., Sun, L., Cheng, L., Wei, Z., & Sun, G. (2020). Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renewable Energy, 160, 26–41.

    Article  Google Scholar 

  • Zhai, B., & Chen, J. (2018). Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China. Science of Total Environment, 635, 644–658.

    Article  Google Scholar 

  • Zhang, W., Dang, H., & Simoes, R. (2018). A new solar power output prediction based on hybrid forecast engine and decomposition model. ISA Transaction, 81, 105–120.

    Article  Google Scholar 

  • Zhang, Y., Chen, B., Pan, G., & Zhao, Y. (2019). A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Conversion and Management, 195, 180–197.

    Article  Google Scholar 

  • Zhang, T., Lv, C., Ma, F., Zhao, K., Wang, H., & O’Hare, G. M. P. (2020). A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform. Neurocomputing, 397, 438–446.

    Article  Google Scholar 

  • Zhang, Y., Zhang, Y., He, K., Li, D., Xu, X., & Gong, Y. (2021). Intelligent feature recognition for STEP-NC-compliant manufacturing based on artificial bee colony algorithm and back propagation neural network. Journal of Manufacturing Systems, 62, 792–799.

    Article  Google Scholar 

  • Zhou, H., Zhang, Y., Yang, L., & Liu, Q. (2018). Short-Term Photovoltaic Power Forecasting Based on Stacking-SVM. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 994–998. IEEE. https://doi.org/10.1109/ITME.2018.00221

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Authors and Affiliations

  1. CEG-IST, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Eric Ofori-Ntow Jnr & Susana Relvas

  2. Faculty of Engineering, University of Mines and Technology, P. O. Box 237, Tarkwa, Ghana

    Eric Ofori-Ntow Jnr

  3. Faculty of Geosciences and Environmental Studies, University of Mines and Technology, P. O. Box 237, Tarkwa, Ghana

    Yao Yevenyo Ziggah

  4. Lisboa E-Nova and Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Maria Joao Rodrigues

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  1. Eric Ofori-Ntow Jnr
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Correspondence to Eric Ofori-Ntow Jnr.

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Ofori-Ntow Jnr, E., Ziggah, Y.Y., Rodrigues, M.J. et al. A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology. Nat Resour Res 31, 1265–1287 (2022). https://doi.org/10.1007/s11053-022-10058-3

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